30 phd-position-in-image-processing Postdoctoral positions at Massachusetts Institute of Technology
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Massachusetts Institute of Technology, The MIT Center for Theoretical Physics - a Leinweber Institute Position ID: MIT-CTP-LI-POSTDOC [#30699] Position Title: Position Type: Postdoctoral Position
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Massachusetts Institute of Technology, The MIT Center for Theoretical Physics - a Leinweber Institute Position ID: MIT-CTP-LI-POSTDOC1 [#30700] Position Title: Position Type: Postdoctoral Position
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Massachusetts Institute of Technology, The MIT Center for Theoretical Physics - a Leinweber Institute Position ID: MIT-CTP-LI-POSTDOC2 [#30701] Position Title: Position Type: Postdoctoral Position
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Posting Description POSTDOCTORAL ASSOCIATE, Civil and Environmental Engineering (CEE) – Howland Lab , is a one-year Postdoctoral Scholar position supported in part by the flagship MIT Climate Grand
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have a PhD in physics, astronomy, or a closely related field at the time of appointment. The position is for a two-year initial term with extension for a third-year contingent upon performance. The start
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will focus on the Thermodynamic task, and two on the Transport task. Job Requirements REQUIRED: PhD in Physics or Mechanical Engineering or related area. The position will remain open until qualified
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. Applicants must have a PhD in Astronomy, Astrophysics, Physics, or a closely related field by the start of the position. The successful applicant must have the ability to carry out an independent research
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Posting Description POSTDOCTORAL ASSOCIATE (2 positions), Center for International Studies (CIS), to conduct policy-relevant research in global development. Will advance research on international
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Massachusetts Institute of Technology, Political Science Position ID: MIT-Political Science-POSTASSOC [#30501] Position Title: Position Type: Postdoctoral Position Location: Cambridge, Massachusetts
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago
-in-the-loop proofreading of automated cell reconstructions, active-learning approaches for efficient annotation, and self-supervision approaches for tokenizing image datasets and cell reconstructions